predict_dimensionality_reduction
Use a pretrained model to predict embeddings.
Usually employed after the train_dimensionality_reduction step.
???+ info “Prediction Model”
To use this step successfully you need to make sure the dataset you’re predicting on is
as similar as possible to the one the model was trained on. We check that the necessary data
types and columns are present, but you should pay attention to how you handled these in the
recipe the model was generated. Any changes might lead to a significant degradation in
model performance.
Usage
The following example shows how the step can be used in a recipe.
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally
columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Configuration
The following parameters can be used to configure the behaviour of the step by including them in
a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).
This step doesn’t expect any configuration.